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  • 标题:Micro Expression Recognition Using the Eulerian Video Magnification Method
  • 本地全文:下载
  • 作者:Elham Zarezadeh ; Mehdi Rezaeian
  • 期刊名称:Brain. Broad Research in Artificial Intelligence and Neuroscience
  • 印刷版ISSN:2067-3957
  • 出版年度:2016
  • 卷号:7
  • 期号:3
  • 页码:43-54
  • 语种:English
  • 出版社:EduSoft publishing
  • 摘要:In this paper we propose a new approach for facial micro expressions recognition. For this purpose the Eulerian Video Magnification (EVM) method is used to retrieve the subtle motions of the face. The results of this method are obtained as in the magnified images sequence. In this study the numerical tests are performed on two databases: Spontaneous Micro expression (SMIC) and Category and Sourcing Managers Executive (CASME). We evaluate our proposed method in two phases using the eigenface method. In phase 1 we recognize the type of a micro expression, for example emotional versus unemotional in SMIC database. Phase 2 classifies the recognized micro expression as negative versus positive in SMIC database and happiness versus disgust in CASME database. The results show that the eigenface method by the EVM method for the retrieval of subtle motions of the face increases the performance of micro expression recognition. Moreover, the proposed approach is more accurate and promising than the previous works in micro expressions recognition.
  • 其他摘要:In this paper we propose a new approach for facial micro expressions recognition. For this purpose the Eulerian Video Magnification (EVM) method is used to retrieve the subtle motions of the face. The results of this method are obtained as in the magnified images sequence. In this study the numerical tests are performed on two databases: Spontaneous Micro expression (SMIC) and Category and Sourcing Managers Executive (CASME). We evaluate our proposed method in two phases using the eigenface method. In phase 1 we recognize the type of a micro expression, for example emotional versus unemotional in SMIC database. Phase 2 classifies the recognized micro expression as negative versus positive in SMIC database and happiness versus disgust in CASME database. The results show that the eigenface method by the EVM method for the retrieval of subtle motions of the face increases the performance of micro expression recognition. Moreover, the proposed approach is more accurate and promising than the previous works in micro expressions recognition.
  • 关键词:Micro Expression Recognition; Eulerian Video Magnification Method; Eigenface Method
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